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Accurate and timely rice mapping at regional scales is essential for global food security monitoring, agricultural policy development, and sustainable intensification efforts. Synthetic Aperture Radar (SAR) provides all-weather, day-and-night observation capabilities, making it particularly suitable for monitoring crop distribution. However, phenology-based methods often face challenges due to diverse cropping practices, and deep learning approaches typically require extensive, high-quality ground truth data that are labor-intensive and region-specific. To address these limitations, we propose an Automated Phenology-Guided Deep Learning framework for Rice Mapping (APDL-Rice), which integrates phenological knowledge with deep learning to eliminate the need for manual sample collection. Leveraging time-series SAR imagery, APDL-Rice automatically extracts phenological signals to generate reliable training samples. The framework was validated across four experimental sites in Guangdong and Heilongjiang, which have different rice cropping practices and agroecological conditions. Results demonstrate that APDL-Rice delivers consistently high performance, with classification accuracies ranging from 83.98% to 94.54%, often surpassing conventional sample-dependent methods. In addition, we investigate the effects of sample quantity and composition on model performance, providing practical insights for deep learning-based rice mapping in sample-constrained scenarios. These findings highlight the method’s strong adaptability across varying cropping systems and its potential for broader application under limited-sample conditions.

Phenology-guided deep learning for automated rice mapping using SAR time-series imagery